• DocumentCode
    637152
  • Title

    Analysis on the number of XCS agents in agent-based computational finance

  • Author

    Nakada, Takashi ; Takadama, K.

  • Author_Institution
    Res. Center, Inst. of Transp. Econ., Japan
  • fYear
    2013
  • fDate
    16-19 April 2013
  • Firstpage
    8
  • Lastpage
    13
  • Abstract
    An agent-based simulation developed as a tool to analyze economic system and social systems since the 1990s. Previous paper reported that the simulation results indicated that the number of agents affects the trading prices and their distributions. To analyze the effect of the number of agents, this paper analyzes the relationship between the number of agents and simulation results using XCS agents for artificial trading. We report the market price fluctuation and population size of internal model by the number of agents. The revealed the following remarkable implications: (1) increasing number of XCS agents does not affect the convergence of population size of all agents; and (2) all agents converge towards approximately form 15 % to 20 %of population size by learning classifier system of XCS agents; and (3) increasing number of XCS agents reduce the variance of the market price.
  • Keywords
    financial data processing; learning (artificial intelligence); pattern classification; pricing; stock markets; XCS agents; agent-based computational finance; agent-based simulation; artificial trading; economic system; learning classifier system; market price fluctuation; market price variance; population size; social systems; trading prices; Adaptation models; Computational modeling; Decision making; Economics; Simulation; Sociology; Statistics; Agent-based Computational Finance; Continuous Double-auction Market; XCS agents;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Computational Intelligence for Financial Engineering & Economics (CIFEr), 2013 IEEE Conference on
  • Conference_Location
    Singapore
  • Type

    conf

  • DOI
    10.1109/CIFEr.2013.6611690
  • Filename
    6611690